Using data from the Seattle Public Library, I will observe trends in
Jane Austen’s book checkouts from three datasets: (1) a dataset of all
checkouts from 2022-2023 (all_checkouts), (2) a dataset of
items checked out at least 10 times a month from 2017-2023
(ten_checkouts), and (3) a dataset of items checked out at
least 5 times a month from 2013-2023 (five_checkouts). I
also plan on analyzing the checkout patterns in the average number of
checkouts (average_checkouts_per_Austen_book), checkout
data for specific books and its format-class during certain months
(most_checkouts_for_emma_digital,
most_checkouts_for_emma_physical), year with the least
number of checkouts for a book
(least_checkouts_for_lady_susan), and total Emma checkouts
from 2022 to 2023 (emma_print_checkouts). By looking at
checkout data for certain books, I hope to gain insight into the
popularity of Austen’s most famous works in relation to time.
When working with this data, I chose to analyze it by a specific author, Jane Austen. I picked Jane Austen because she is a very famous author, and since two of the dataframes used were based on books checked out at least 5 or 10 times a month, I wanted to choose a popular author so that there would be sufficient data to work with since using data from just 2022 and 2023 is quite limited. The areas I wanted to analyze were things like the average checkouts per book to determine her most popular books, the month with the most “Emma” ebook checkouts, the month with the most “Emma” print book checkouts, the Checkout Year for the least checkouts for the book “Lady Susan,” and the number of total Emma checkouts from 2022 to 2023.
When finding these values, I obtained some interesting results for
the month with the most “Emma print book” and the Checkout Year. When
analyzing the most popular “Emma print book” checkout month, I received
a value of 0, but this was because there were no physical copies
available - only digital. For digital, the most popular months were
January and August. For the Checkout Year for the least checkouts for
the book “Lady Susan,” I had originally used
(all_checkouts), but changed it to
(five_checkouts) because it only showed 2022, as there were
only two possible values originally. After changing it, I got 2016,
2017, and 2022.
For average checkouts per book, Emma was the most popular at a little over 39 checkouts, and Pride and Prejudice was the second most popular at around 37 checkouts. The least checked-out book was Raison et Sensibilité, which is the French version of her book Sense and Sensibility (the fourth most checked-out book). It is likely last because it is in French, and Seattle may not have a lot of people who prefer reading in French. I also analyzed the number of total Emma checkouts from 2022 to 2023 and obtained a total of 292.
This data was published by the Seattle Public Library.
The parameters of the data are UsageClass, CheckoutType,
MaterialType, CheckoutYear, CheckoutMonth, Checkouts, Title, ISBN,
Creator, Subjects, Publisher, and PublicationYear. ##### How was the
data collected or generated?
The data was collected by the Seattle Public Library storing data each
time a person checks out a book. The data is usually updated monthly
(with the last update being February 6, 2023).
##### Why was the data collected?
The data was likely collected for inventory purposes, so they can order
more popular books or shorten checkout durations for popular books
during historically popular checkout months.
##### What, if any, ethical questions do you need to consider when
working with this data?
Since the Seattle Public Library is a government entity, there can be
privacy concerns over the government having access to the reading trends
of the public.
##### What are possible limitations or problems with this data? (at
least 200 words)
There are some possible limitations to consider when examining this
data, such as the time period it covers. As the data goes back to 2005,
the original file was huge and quite hard to work with all at once.
Therefore, each of the data frames used was condensed to checkouts made
only between 2022-2023 (all), 2017-2023 (ten checkouts), or 2013-2023
(five checkouts), which cover a span of just a few years. Since I am
working with data from a specific author, it makes looking at yearly
data difficult. For example, in my
(least_checkouts_for_lady_susan), I had originally used the
(all_checkouts) data frame, but changed it to the
(five_checkouts) data frame because the value just showed
2022 originally. Since there were only two possible years in
(all_checkouts), it doesn’t create unique/interesting
analysis. Additionally, since the amount of data collected from each
borrower is limited (due to ethical reasons), the scope of the data is
limited and may not show some other interesting details. Some other
challenges with this data are the risks of errors in the data. A few
examples of this include unreturned books causing a decline in the
number of checkouts recorded, books being checked out multiple times by
the same person, or technical errors.
## `summarise()` has grouped output by 'title_group'. You can override using the
## `.groups` argument.This time series chart shows the total checkouts for Jane Austen books, categorized by title. The chart reveals that Pride and Prejudice was the most popular book by Austen in 2022, with September 2022 and January 2023 being the most popular months, with 239 checkouts. May 2022, with 132 checkouts, was the least popular month for Pride and Prejudice. The reasons for the spike may include people having more leisure time to read during holiday months, an increase in reading activities in January due to reading goals, or the book being commonly used as required reading in many US English classes.
## `summarise()` has grouped output by 'MaterialType'. You can override using the
## `.groups` argument.